code: def get_model_tokenizer(model_name):
      if model_name == "llama":
          return LlamaForCausalLM, LlamaTokenizer, Trainer
      elif model_name == "chatglm":
          return AutoModel, AutoTokenizer, Seq2SeqTrainer
      else:
          return AutoModelForCausalLM, AutoTokenizer, Trainer
  
file_path: DeepKE/example/llm/InstructKGC/src/utils/general_utils.py
imports: import os
  import random
  import time
  import numpy
  import torch
  from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizer, Trainer, Seq2SeqTrainer
label_desc: Use image node_id 1 for calling a global function with example usage: get_model_tokenizer(model_name) and returns: LlamaForCausalLM,  LlamaTokenizer,  Trainer, AutoModel,  AutoTokenizer,  Seq2SeqTrainer, AutoModelForCausalLM,  AutoTokenizer,  Trainer